Impact of Egocentric Survey Design on Estimable Network Features
Pavel N. Krivitsky, Michał Bojanowski, Martina MorrisEgocentric sampling of networks selects a subset of nodes ("egos") and collects information from them on themselves and their immediate network neighbours ("alters"), leaving the rest of the nodes in the network unobserved. This design is popular because it is relatively inexpensive to implement and can be integrated into standard survey sampling designs. Recent work has shown that such data can be used to estimate a class of Exponential-family Random Graph Models (ERGMs) with principled statistical inference, and the fitted models can in turn be used to simulate complete networks of arbitrary size that are consistent with the observed sample data. This is accomplished by using the sampled data and standard survey sampling inference tools to reconstruct the network statistics needed by ERGM and quantify their uncertainty, and then transfer their property to the final ERGM estimates. In this work, we discuss how design choices for egocentric network studies impact statistical estimation and inference. The design choices include both sampling strategies (for egos and alters) and measurement strategies (for ego and alter attributes, and for ego–alter and alter–alter ties). We discuss the importance of harmonising measurement specifications across egos and alters, and conduct simulation studies to demonstrate the impact of sampling design on statistical inference, specifically stratified sampling and degree censoring.